Araştırma Makalesi
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An adaptive neuro-fuzzy inference system-based approach for daily load curve prediction

Yıl 2018, Cilt: 2 Sayı: 3, 115 - 126, 30.09.2018
https://doi.org/10.30521/jes.434224

Öz

Forecasting future electricity demand is one of the most important areas
in electrical engineering, due to its vital role for secure and profitable
operations in power systems. In recent years, the advent of new concepts and
technologies such as deregulation of electricity market, smart grids, electric
cars and renewable energy integration have introduced great challenges for
power system managers and consequently, the field of electric load forecasting
becomes more and more important. This paper describes the use of an adaptive
neuro-fuzzy inference system approach for daily load curve prediction. The
methodology we propose uses univariate modeling to recognize the daily and
weekly patterns of the electric load time series as a basis for the forecast.
Results from real-world case study based on the electricity demand data in
France are presented in order to illustrate the proficiency of the proposed
approach. With an average mean absolute percentage error of 2.087%, the
effectiveness of the proposed model is clearly revealed.

Kaynakça

  • Parlos AG, Oufi E, Muthusami J, Patton AD, Atiya AF. Development of an intelligent long-term electric load forecasting system. In: ISAP '96. International Conference on Intelligent Systems Applications to Power Systems; 28 Jan-2 Feb 1996: IEEE, Orlando, FL, USA, pp. 288-292.
  • Parlos AG, Patton AD. Long-term electric load forecasting using a dynamic neural network architecture. In: APT 93. Joint International Power Conference Athens Power Tech; 5-8 Sep 1993: IEEE, Athens, Greece, pp. 816-820.
  • Dalvand MM, Azami S, Tarimoradi H. Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks. In: UPEC 2008. 43rd International Universities Power Engineering Conference; 1-4 Sept 2008: IEEE, Padova, Italy, pp. 1-4.
  • Hong, T, Wilson, J, Xie, J. Long Term Probabilistic Load Forecasting and Normalization with Hourly Information. IEEE Transactions on Smart Grid 2014; 5(1): 456-462 <https://doi.org/10.1109/TSG.2013.2274373>
  • Jain A, Satish B. Short term load forecasting by clustering technique based on daily average and peak loads. In: PES '09. IEEE General Meeting Power & Energy Society; 26-30 July 2009: IEEE, Calgary, AB, Canada, pp. 1-7.
  • Amjady, N, Keynia, F. Mid-term load forecasting of power systems by a new prediction method. Energy Conversion and Management 2008; 49(10): 2678–2687 <https://doi.org/10.1016/j.enconman.2008.04.008>
  • Jaramillo-Morán, MA, Romera, EG, Fernández, DC. Monthly electric demand forecasting with neural filters. International Journal of Electrical Power & Energy Systems 2013; 49: 253–263 <https://doi.org/10.1016/j.ijepes.2013.01.019>
  • Torkzadeh R, Mirzaei A, Mirjalili MM, Anaraki AS, Sehhati MR, Behdad F. Medium term load forecasting in distribution systems based on multi linear regression & principal component analysis: A novel approach. In: EPDC 2014. 19th Conference on Electrical Power Distribution Networks; 6-7 May 2014: IEEE, Tehran, Iran, pp. 66-70.
  • Laouafi, A, Mordjaoui, M, Haddad, S, Boukelia, TE, Ganouche, A. Online electricity demand forecasting based on an effective forecast combination methodology. Electric Power Systems Research 2017; 148: 35-47 <https://doi.org/10.1016/j.epsr.2017.03.016>
  • Charytoniuk, W, Chen, MS. Very Short-Term Load Forecasting Using Artificial Neural Networks. IEEE Transactions on Power Systems 2000; 15: 263-268 <https://doi.org/10.1109/59.852131>
  • Hesham, K. Electric load forecasting: Literature survey and classification of methods. International Journal of Systems Science 2012; 33(1): 23-34 <https://doi.org/10.1080/00207720110067421>
  • Box GEP, Jenkins JM. Time Series Analysis: Forecasting and Control. In: Holden-Day, San Francisco, 1976.
  • Hagan, MT, Behr, SM. The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems 1987; 2(3): 785-791 <https://doi.org/10.1109/TPWRS.1987.4335210>
  • Papalexopoulos, AD, Hesterberg, TC. A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems 1990; 5: 1535-1547 <https://doi.org/10.1109/PICA.1989.39025>
  • Taylor, JW. Short-Term Load Forecasting with Exponentially Weighted Methods. IEEE Transactions on Power Systems 2012; 27(1): 458-464 <https://doi.org/10.1109/TPWRS.2011.2161780>
  • Park, DC, El-Sharkawi, MA, Marks, RJII, Atlas, LE, Damborg, MJ. Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems 1991; 6(2): 442–449 <https://doi.org/10.1109/59.76685>
  • Ranaweera, DK, Hubele, NF, Karady, GG. Fuzzy logic for short term load forecasting. International Journal of Electrical Power & Energy Systems 1996; 18(4): 215-222 <https://doi.org/10.1016/0142-0615(95)00060-7>
  • Zhang, X, Wang, J. A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns. Applied Soft Computing 2018; 65: 478-494 <https://doi.org/10.1016/j.asoc.2018.01.017>
  • Mordjaoui, M, Haddad, S, Medoued, A, Laouafi, A. Electric load forecasting by using dynamic neural network. International Journal of Hydrogen Energy 2017; 42(28): 17655-17663 <https://doi.org/10.1016/j.ijhydene.2017.03.101>
  • Palit AK, Anheier W, Popovic D. Electrical Load Forecasting Using a Neural-Fuzzy Approach. In: Chiong R, Dhakal S, editors. Natural Intelligence for Scheduling, Planning and Packing Problems, Springer-Verlag, Berlin Heidelberg, 2009. pp. 145-173.
  • Laouafi, A, Mordjaoui, M, Laouafi, F, Boukelia, TE. Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. International Journal of Electrical Power & Energy Systems 2016; 77: 136-144 <https://doi.org/10.1016/j.ijepes.2015.11.046>
  • Palit AK, Popovic D. Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer-Verlag Berlin, Heidelberg, 2005.
  • Sumathi S, Surekha P. Computational intelligence paradigms theory and applications using MATLAB. Taylor and Francis Group, LLC, 2010.
  • Jang, JSR. ANFIS: Adaptive network based fuzzy inference system. IEEE Transactions on systems, Man, and, Cybernetics 1993. 23(3): 665-685 <https://doi.org/10.1109/21.256541>
  • Laouafi A, Mordjaoui M, Dib D. One-Hour Ahead Electric Load Forecasting Using Neuro-fuzzy System in a Parallel Approach. In: Azar AT, Vaidyanathan S, editors. Computational Intelligence Applications in Modeling and Control, Cham, Switzerland: Springer International Publishing, 2015. pp. 95-121.

Yıl 2018, Cilt: 2 Sayı: 3, 115 - 126, 30.09.2018
https://doi.org/10.30521/jes.434224

Öz

Kaynakça

  • Parlos AG, Oufi E, Muthusami J, Patton AD, Atiya AF. Development of an intelligent long-term electric load forecasting system. In: ISAP '96. International Conference on Intelligent Systems Applications to Power Systems; 28 Jan-2 Feb 1996: IEEE, Orlando, FL, USA, pp. 288-292.
  • Parlos AG, Patton AD. Long-term electric load forecasting using a dynamic neural network architecture. In: APT 93. Joint International Power Conference Athens Power Tech; 5-8 Sep 1993: IEEE, Athens, Greece, pp. 816-820.
  • Dalvand MM, Azami S, Tarimoradi H. Long-term load forecasting of Iranian power grid using fuzzy and artificial neural networks. In: UPEC 2008. 43rd International Universities Power Engineering Conference; 1-4 Sept 2008: IEEE, Padova, Italy, pp. 1-4.
  • Hong, T, Wilson, J, Xie, J. Long Term Probabilistic Load Forecasting and Normalization with Hourly Information. IEEE Transactions on Smart Grid 2014; 5(1): 456-462 <https://doi.org/10.1109/TSG.2013.2274373>
  • Jain A, Satish B. Short term load forecasting by clustering technique based on daily average and peak loads. In: PES '09. IEEE General Meeting Power & Energy Society; 26-30 July 2009: IEEE, Calgary, AB, Canada, pp. 1-7.
  • Amjady, N, Keynia, F. Mid-term load forecasting of power systems by a new prediction method. Energy Conversion and Management 2008; 49(10): 2678–2687 <https://doi.org/10.1016/j.enconman.2008.04.008>
  • Jaramillo-Morán, MA, Romera, EG, Fernández, DC. Monthly electric demand forecasting with neural filters. International Journal of Electrical Power & Energy Systems 2013; 49: 253–263 <https://doi.org/10.1016/j.ijepes.2013.01.019>
  • Torkzadeh R, Mirzaei A, Mirjalili MM, Anaraki AS, Sehhati MR, Behdad F. Medium term load forecasting in distribution systems based on multi linear regression & principal component analysis: A novel approach. In: EPDC 2014. 19th Conference on Electrical Power Distribution Networks; 6-7 May 2014: IEEE, Tehran, Iran, pp. 66-70.
  • Laouafi, A, Mordjaoui, M, Haddad, S, Boukelia, TE, Ganouche, A. Online electricity demand forecasting based on an effective forecast combination methodology. Electric Power Systems Research 2017; 148: 35-47 <https://doi.org/10.1016/j.epsr.2017.03.016>
  • Charytoniuk, W, Chen, MS. Very Short-Term Load Forecasting Using Artificial Neural Networks. IEEE Transactions on Power Systems 2000; 15: 263-268 <https://doi.org/10.1109/59.852131>
  • Hesham, K. Electric load forecasting: Literature survey and classification of methods. International Journal of Systems Science 2012; 33(1): 23-34 <https://doi.org/10.1080/00207720110067421>
  • Box GEP, Jenkins JM. Time Series Analysis: Forecasting and Control. In: Holden-Day, San Francisco, 1976.
  • Hagan, MT, Behr, SM. The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems 1987; 2(3): 785-791 <https://doi.org/10.1109/TPWRS.1987.4335210>
  • Papalexopoulos, AD, Hesterberg, TC. A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems 1990; 5: 1535-1547 <https://doi.org/10.1109/PICA.1989.39025>
  • Taylor, JW. Short-Term Load Forecasting with Exponentially Weighted Methods. IEEE Transactions on Power Systems 2012; 27(1): 458-464 <https://doi.org/10.1109/TPWRS.2011.2161780>
  • Park, DC, El-Sharkawi, MA, Marks, RJII, Atlas, LE, Damborg, MJ. Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems 1991; 6(2): 442–449 <https://doi.org/10.1109/59.76685>
  • Ranaweera, DK, Hubele, NF, Karady, GG. Fuzzy logic for short term load forecasting. International Journal of Electrical Power & Energy Systems 1996; 18(4): 215-222 <https://doi.org/10.1016/0142-0615(95)00060-7>
  • Zhang, X, Wang, J. A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns. Applied Soft Computing 2018; 65: 478-494 <https://doi.org/10.1016/j.asoc.2018.01.017>
  • Mordjaoui, M, Haddad, S, Medoued, A, Laouafi, A. Electric load forecasting by using dynamic neural network. International Journal of Hydrogen Energy 2017; 42(28): 17655-17663 <https://doi.org/10.1016/j.ijhydene.2017.03.101>
  • Palit AK, Anheier W, Popovic D. Electrical Load Forecasting Using a Neural-Fuzzy Approach. In: Chiong R, Dhakal S, editors. Natural Intelligence for Scheduling, Planning and Packing Problems, Springer-Verlag, Berlin Heidelberg, 2009. pp. 145-173.
  • Laouafi, A, Mordjaoui, M, Laouafi, F, Boukelia, TE. Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. International Journal of Electrical Power & Energy Systems 2016; 77: 136-144 <https://doi.org/10.1016/j.ijepes.2015.11.046>
  • Palit AK, Popovic D. Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer-Verlag Berlin, Heidelberg, 2005.
  • Sumathi S, Surekha P. Computational intelligence paradigms theory and applications using MATLAB. Taylor and Francis Group, LLC, 2010.
  • Jang, JSR. ANFIS: Adaptive network based fuzzy inference system. IEEE Transactions on systems, Man, and, Cybernetics 1993. 23(3): 665-685 <https://doi.org/10.1109/21.256541>
  • Laouafi A, Mordjaoui M, Dib D. One-Hour Ahead Electric Load Forecasting Using Neuro-fuzzy System in a Parallel Approach. In: Azar AT, Vaidyanathan S, editors. Computational Intelligence Applications in Modeling and Control, Cham, Switzerland: Springer International Publishing, 2015. pp. 95-121.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Abderrezak LAOUAFİ
Université 20 Août 1955-Skikda
0000-0002-5385-7062
Algeria


Mourad MORDJAOUİ
Université 20 Août 1955-Skikda
0000-0002-5195-6016
Algeria


Taqiy Eddine BOUKELİA Bu kişi benim
Jijel University
0000-0002-6683-0491
Algeria

Yayımlanma Tarihi 30 Eylül 2018
Yayınlandığı Sayı Yıl 2018 Cilt: 2 Sayı: 3

Kaynak Göster

Vancouver Laouafi A. , Mordjaoui M. , Boukelia T. E. An adaptive neuro-fuzzy inference system-based approach for daily load curve prediction. Journal of Energy Systems. 2018; 2(3): 115-126.

Journal of Energy Systems is the official journal of 

European Conference on Renewable Energy Systems (ECRES8756 and


Electrical and Computer Engineering Research Group (ECERG)  8753


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